The use of “stress waves” and their analysis is the topic of a number of patent applications, which will be briefly described hereinbelow:
U.S. Pat. No. 4,530,240, titled “Method and Apparatus for Diagnosing Machine Condition, and which is incorporated herein by reference, teaches a means for predicting machine failure by monitoring stress waves produced by friction and shock events.
U.S. Pat. No. 5,852,793, titled: METHOD AND APPARATUS FOR PREDICTIVE DIAGNOSIS OF MOVING MACHINE PARTS, and incorporated herein by reference, describes Stress Wave Analysis (SWAN) technology resulting from more than a decade of research and development activity. The technology includes analog and digital hardware designs, as well as software, that significantly increase signal to noise ratio, implement SWAN technology in low cost PC based platforms, and provide data logging and predictive maintenance capability. The disclosed method includes new ways of displaying SWAN data for simplified analysis, as well as Time Domain Feature Extraction software that provides “intelligent data compression” for use with Artificial Intelligence software.
U.S. Pat. No. 6,351,713, titled: DISTRIBUTED STRESS WAVE ANALYSIS SYSTEM, and incorporated herein by reference, discloses a next generation of SWAN products, which combine Stress Wave Analysis with Artificial Intelligence to provide automation to the interpretation of SWAN data. This improvement provides a further reduction in the skill levels and training required to use SWAN technology for accurate predictive maintenance, and extends SWAN capabilities for fault location/isolation and remaining useful life projection. A Frequency Domain Feature Extraction method and a proprietary Data Fusion Architecture are disclosed for providing very accurate fault detection, with very low probability of false alarms. The hardware designs described in this patent provide additional improvement of signal to noise ratio, while significantly reducing the size, weight, and power consumption of SWAN hardware, so that it becomes more practical for a variety of mobile and fixed base applications.
U.S. Pat. No. 6,499,350 titled: FOREIGN OBJECT DETECTION (FOD), and incorporated herein by reference, teaches the use of a specialized hardware implementation of SWAN technology for application to turbo machinery, which can be seriously damaged by the ingestion of foreign objects. The disclosed design is applicable for airborne, marine, and ground based applications.
U.S. Pat. No. 6,684,700 titled: STRESS WAVE SENSOR, incorporated herein by reference, defines functional performance requirements for a sensor specifically designed to detect stress waves. This reference also defines the quantitative relationships between the sensor specifications and the analog signal conditioning that is used to filter, amplify, and demodulate the sensor's broad band output.
U.S. Pat. No. 6,553,839 titled: METHOD FOR STIMULATING A SENSOR AND MEASURING THE SENSOR'S OUTPUT OVER A FREQUENCY RANGE and incorporated by reference, describes a calibration technique tailored to the peculiar functional specifications of certain stress wave sensors.
U.S. Pat. No. 6,679,119 titled: MULTI-FUNCTION SENSOR, and incorporated herein by reference, teaches that, for many predictive maintenance applications, SWAN and vibration analysis are complimentary technologies. The sensor described in this patent provides electrical signals proportional to both vibration and stress waves from a single device. This multi-function sensor significantly reduces cost, weight and power requirements compared to separate sensors. This device is applicable for both airborne and industrial applications.
Aerodynamic stall within the compressor and turbine sections of a gas turbine engine has always been a major concern of turbine engine manufactures and users. Aerodynamic stall can lead to sudden and complete loss of power, flameout, and unplanned shutdown, often with catastrophic consequences. Stalls can also cause undetectable High Cycle Fatigue (HCF) damage to the fan, compressor, and turbine blades in a turbine engine. This fatigue leaves a window for the damage to propagate into a catastrophic failure that would endanger lives and/or equipment.
Current attempts to provide stall warning are based upon applying thermodynamic modeling to temperature and pressure measurements made at various points in the engine gas path (Gas Path Analysis), but have been less than satisfactory.
Accordingly, the prior art, fails either alone or in combination with other references, to teach or suggest any apparatus or processes applied to detecting faults or stall conditions in aircraft engines. The prior art does not address, disclose or illustrate many of the components, software, functions or benefits of the instant turbine engine stall warning system, nor the hybrid functionality.
It would be useful to adapt various SWAN techniques to the problem of turbine engine stall problems.